Insurance Samadhan - NLP Engineer - Deep Learning/Machine Learning (2-5 yrs)

Job Description :

We are seeking an experienced NLP Engineer with a strong background in Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). The ideal candidate will have a proven track record in implementing NLP solutions and a solid understanding of business objectives, enabling them to translate these into data science problems.

Responsibilities :

End-to-End NLP Pipeline Development: Work on various components of the NLP and Natural Language Generation (NLG) pipeline, including data preparation, model learning, and inference for applications like speech-to-text, intent recognition, named entity recognition (NER), and dialogue engineering.

Classical NLP & Rule-Based Systems :

- Apply classical NLP techniques (rule-based NLP) and integrate them into modern ML/DL workflows.

Text Analytics & Sequence Modeling :

- Lead efforts in text analytics, including sentiment analysis, topic modeling, entity extraction, and language modeling using sequence learning models like RNN, LSTM, and GRU.

Model Building & Deployment :

- Implement state-of-the-art algorithms such as TF-IDF, word2vec, embeddings, transformers, and work with libraries like spaCy, NLTK, and LLMs for production-level solutions.

Collaboration & Problem Formulation :

- Work closely with business stakeholders to understand their objectives and define the problem as a data science task, aligning technical solutions with business needs.

Project Management :

- Take ownership of NLP projects, from research and prototyping to deployment, ensuring successful delivery on at least two significant projects involving NLP, ML/DL.

Requirement :

- 2+ years of proven experience in ML, DL, and NLP.

- At least 1 year of experience in Classical NLP (rule-based systems).

- Expertise in Python programming with proficiency in one of the DL frameworks, such as PyTorch, TensorFlow (TF), or Keras.

- Hands-on experience with transformers, TF-IDF, word2vec, word embeddings, and topic modeling techniques.

- Familiarity with NLP libraries such as spaCy, NLTK, and expertise in NER, LLMs, and sequence learning models.

- Experience in speech-to-text, intent recognition, and dialogue engineering.

- Ability to understand and solve complex business problems using data science techniques.